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Heat flow, log-concavity, and Lipschitz transport maps

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arxiv 2404.15205 v2 pith:JJERQKVQ submitted 2024-04-23 math.AP math.PR

classification math.APmath.PR
keywords log-hessiangaussianheatinitiallipschitztransportuniformadmits
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In this paper we derive estimates for the Hessian of the logarithm (log-Hessian) for solutions to the heat equation. For initial data in the form of log-Lipschitz perturbation of strongly log-concave measures, the log-Hessian admits an explicit, uniform (in space) lower bound. This yields a new estimate for the Lipschitz constant of a transport map pushing forward the standard Gaussian to a measure in this class. Further connections are discussed with score-based diffusion models and improved Gaussian logarithmic Sobolev inequalities. Finally, we show that assuming only fast decay of the tails of the initial datum does not suffice to guarantee uniform log-Hessian upper bounds.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Mixing Time of the Proximal Sampler in Relative Fisher Information via Strong Data Processing Inequality

    cs.IT 2025-02 accept novelty 7.0 of 10

    The Proximal Sampler has exponential convergence in relative Fisher information for strongly log-concave targets, matching the rate of continuous-time Langevin dynamics.

  2. Structured drift design for denoising diffusion models

    math.ST 2026-06 unverdicted novelty 6.0 of 10

    Proposes GOU process with anisotropic drift to embed data geometry in diffusion models, claiming better mode separation, correlation preservation, and convergence than isotropic baselines.

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